Imaging biomarkers from AI federated learning

This MRFF project seeks to build a novel, hybrid AI learning ecosystem to generate clinically-relevant biomarkers of disease progression for the common, disabling neurological condition, multiple sclerosis (MS).  The MSBASE-XNAT imaging repository and I-MED clinical radiology site data will respectively form the key components of a unique central-federated AI learning environment, yielding algorithms that, validated in a clinical neurology environment, will set a benchmark in diagnostic MS imaging; track subclinical progression of the disease; direct therapeutic strategy; and mine hitherto untapped quantitative imaging data.

This project involves close collaboration with Prof Michael Barnett and Dr Chenyu Tim Wang from the Faculty of Medicine and Health.


Prof Stefan B. Williams – Director, Digital Sciences Initiative
Faculty of Engineering, University of Sydney NSW 2006 Australia
+61 2 9351 8152